bioRxivAnimal Behavior & CognitionPreprint
Preoptic transcriptomes during courtship reveal the molecular basis of behavioral coordination in a highly social cichlid
Abstract
Human and non-human animals often coordinate their behavior, a phenomenon thought to strengthen relationships by enabling sequential assessment of an interaction partners quality as a mate or rival, a process that directly affects evolutionary fitness. There is increasing evidence that behavioral coordination is reflected by synchronized neural activity of the on interacting partners, though few studies have examined synchronization at the level of gene expression. Here, we use Burtons Mouthbrooder cichlid, Astatotilapia burtoni, to examine how neural transcriptomes are synchronized while male-female pairs engaged in variable amounts of reproductive behavior. A. burtoni females naturally display highly coordinated reproductive behavior with a male partner only directly prior to spawning, yet this behavior can be experimentally induced with exogenous prostaglandin F2 alpha (PGF2a), which also acts by releasing pheromones to promote male courtship behavior. We quantified reproductive behavior and circulating sex steroid hormones of PGF2a-treated pairs. We then used transcriptome analysis of the preoptic area - a key node in the regulation of reproductive behavior across vertebrates - and identified gene co-expression modules that showed sex-specific activity in modulating reproductive behavior. Our results, which implement a novel approach to measure transcriptome synchronization, demonstrate a remarkable degree of male-female POA transcriptome synchronization that depends largely on the reproductive responsiveness of the female, mirroring how natural reproduction is driven by female behavior. Together, our work advances our understanding of the molecular mechanisms by which interacting individuals align their actions and how they experience social interaction.
- organism: Arabidopsis
- assay: scRNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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